Every executive chasing meaningful AI outcomes eventually runs into the same wall: the data layer can’t keep up.
GPU investments look great on paper. Data science teams build proof-of-concepts that impress in demos. But when it’s time to operationalize, to scale models, to serve inference at speed, to push through massive unstructured pipelines, throughput, latency, and metadata handling quietly become the limiting factors.
That’s why Dell’s latest PowerScale and ObjectScale release is important. Not because it adds another set of technical features, but because it fundamentally changes how fast organizations can move from idea to impact.
Here's what matters at the business level...
The new parallel I/O capabilities, distributed metadata architecture, and pNFS support mean PowerScale can finally feed modern AI systems at the pace they require.
This is not about raw storage specs. It’s about removing waiting… waiting for training data loads, waiting for large model context retrieval, waiting for high volume pipelines to flush.
With the new architecture achieving up to 19× faster Time to First Token for full context inference windows, organizations can finally use large context models without watching GPU clusters idle.
Business outcome:
• AI teams deploy faster
• Models serve results sooner
• Project cycles shrink
• TTV accelerates
Executives often tell us some version of the same thing:
“We invested in GPUs… but we’re not seeing GPU level performance.”
In almost every case, the issue isn’t the compute layer.
It’s the data layer starving it.
With PowerScale’s upgraded data paths, NVIDIA NIXL support for KV-cache offload, and massively parallel reads, the bottleneck disappears. You unlock the performance you expected when you built the AI cluster.
Business outcome:
• Higher ROI on AI infrastructure
• Reliable, predictable performance
• No more overprovisioning compute to compensate for slow storage
ObjectScale’s enhancements go beyond “object storage.”
With S3 tables and emerging vector search APIs, Dell is effectively turning object stores into AI-ready unstructured data engines.
Why that matters:
Most enterprises don’t have clean knowledge graphs, perfectly curated feature stores, or neat structured data marts. They have petabytes of files, images, logs, documents, and blobs.
This release lets organizations do two things they’ve wanted for years:
Business outcome:
AI isn’t bolted onto the data estate, it’s integrated into it.
Our Clients rarely want “more systems.” They want fewer.
They want consolidation without losing capability.
This release achieves something that didn’t really exist ten years ago:
A unified file + object platform that can ingest, process, and serve high-bandwidth, low-latency workloads without building separate islands of infrastructure.
That’s meaningful for several real industry scenarios:
Business outcome:
• Less infrastructure sprawl
• Lower operational complexity
• A straighter path from data capture to data insight
This part often gets overlooked but matters most to executives:
PowerScale’s capabilities now run in a software only model, including on qualified PowerEdge servers.
That means:
Business outcome:
You can evolve your data layer at the pace of your business — not the other way around.
AI has created a new competitive ceiling: you can only innovate as fast as your data infrastructure allows.
Most organizations aren’t constrained by ideas or talent... they’re constrained by throughput, latency, and the inability to serve massive context windows efficiently.
Dell’s new PowerScale/ObjectScale release is one of the clearest signals that the market is shifting away from “legacy storage with AI wrappers” toward true AI native data architectures.
And for leaders making decisions about where AI fits into the business, here’s the shift that matters:
This isn’t about faster storage. It’s about faster organizations.
We work closely with enterprises that are modernizing their data and AI platforms, and one theme shows up consistently: architecture determines outcomes.
Our role is helping organizations design the end-to-end data pipelines, governance frameworks, platform choices, and operational models that turn technology investments into measurable business impact.
If your organization is evaluating how to scale AI, unify unstructured data, or modernize the data layer beneath your analytics and AI workloads, we’re here to guide that conversation.